A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images
Abstract
:1. Introduction
2. MRI Distortions
2.1. Spike (Herringbone) Artifact
2.2. Zipper Artifact
2.3. Ghosting
2.4. Blurring
2.5. Aliasing Artifacts
2.6. Gibbs Effect
2.7. Slice-Overlap Artifact
2.8. Gradient-Related Distortion
2.9. Parallel Imaging Artifact
2.10. Susceptibility Effect
3. NR-IQA Approaches
3.1. A Two-Step Automated Quality Assessment for Liver MR Images Based on Convolutional Neural Network
3.2. Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI Consistency
3.3. No-Reference Image Quality Assessment of T2-Weighted Magnetic Resonance Images in Prostate Cancer Patients
3.4. Two-Stage Multi-Modal MR Images Fusion Method Based on Parametric Logarithmic Image Processing Model
3.5. Hierarchical Non-Local Residual Networks for Image Quality Assessment of Pediatric Diffusion MRI with Limited and Noisy Annotations
3.6. HyS-Net
3.7. QEMDIM
3.8. AQASB
3.9. Multi-Class Cardiovascular Magnetic Resonance Image Quality Assessment Using Unsupervised Domain Adaptation
3.10. MRIQC
3.11. Brain and Cardiac MRI Images in Multi-Center Clinical Trials
3.12. Modified Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE)
3.13. R50GR18
3.14. Entropy-Based Magnetic Resonance Image Quality Assessment Measure (ENMIQA)
3.15. No-Reference Image Quality Assessment of Magnetic Resonance Images with High-Boost Filtering and Local Features (NOMRIQA)
3.15.1. PSNR/SNR
3.15.2. Maximum Difference
3.15.3. Normalized Cross-Correlation
4. Evaluation of IQA Models
4.1. Databases
4.1.1. OpenfMRI
4.1.2. ADNI
4.1.3. National Resource for Quantitative Functional MRI
4.1.4. Autism Brain Imaging Data Exchange (ABIDE)
4.1.5. 1.5 T T2-Weighted MR Image Databases: DB1, DB2
4.2. Evaluation Protocol
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Method | Approach and Features | Number of Features | Datasets |
---|---|---|---|
A two-step automated quality assessment for liver MR images based on convolutional neural network [53] |
| - | Not defined in the paper |
Semi-supervised learning for fetal brain MRI quality assessment with ROI consistency [7] |
| - | Scans acquired at Boston Children’s Hospital |
No-reference image quality assessment of T2-weighted magnetic resonance images in prostate cancer patients [54] |
| - |
|
Two-stage multi-modal MR images fusion method based on parametric logarithmic image processing (PLIP) model [55] |
| - |
|
Hierarchical non-local residual networks for image quality assessment of pediatric diffusion MRI with limited and noisy annotations [43] |
| - |
|
HyS-net [45] |
| - |
|
QEMDIM [48] |
| 20 | |
AQASB [60] |
| - |
|
Multi-class cardiovascular magnetic resonance image quality assessment using unsupervised domain adaptation [49,61] |
| 512 |
|
MRIQC [63] |
| 64 | |
Brain and cardiac MRI images in multi-center clinical trials [65] |
| The number of features depends on the image |
|
Modified-BRISQUE [46] |
| 36 |
|
R50GR18 [50] |
| 3584 | |
ENMIQA [51] |
| 1 |
|
NOMRIQA [52] |
| 3840 |
Name | Year | No. of Images | Link (Accessed on 27 April 2022) |
---|---|---|---|
OpenfMRI | 2010 | Not specified/repository of datasets | openfmri.org |
ADNI-1 | 2004–2010 | 200 elderly controls, 400 MCI, 200 AD | adni.loni.usc.edu |
ADNI-GO | 2009–2011 | Existing ADNI-1 + 200 early MCI | adni.loni.usc.edu |
ADNI-2 | 2011–2017 | Existing ADNI-1 and ADNI-GO + additional images | adni.loni.usc.edu |
ADNI-3 | 2017–2022 | Existing ADNI-1, ADNI-GO, ADNI-2 + additional images | adni.loni.usc.edu |
ABIDE I | 2012 | 1112 datasets | fcon1000.projects.nitrc.org |
ABIDE II | 2016 | Existing ABIDE I and 1000 datasets | fcon1000.projects.nitrc.org |
DB1 | 2020 | 70 | marosz.kia.prz.edu.pl/ENMIQA.html |
DB2 | 2020 | 240 | marosz.kia.prz.edu.pl/NOMRIQA.html |
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Stępień, I.; Oszust, M. A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images. J. Imaging 2022, 8, 160. https://doi.org/10.3390/jimaging8060160
Stępień I, Oszust M. A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images. Journal of Imaging. 2022; 8(6):160. https://doi.org/10.3390/jimaging8060160
Chicago/Turabian StyleStępień, Igor, and Mariusz Oszust. 2022. "A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images" Journal of Imaging 8, no. 6: 160. https://doi.org/10.3390/jimaging8060160
APA StyleStępień, I., & Oszust, M. (2022). A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images. Journal of Imaging, 8(6), 160. https://doi.org/10.3390/jimaging8060160